Human Resources Analytics is on the rise worldwide. Let’s take a deeper look at the first step of analytics, the data. Is your human resources data suitable for analytics?
Data cleaning and consolidation is a crucial step in the process of HR analytics. It helps to ensure that the data used for analysis is accurate, consistent, and complete. This is important because the quality of the data used for analysis directly impacts the accuracy and usefulness of the insights that are generated.
There are several ways to effectively clean and consolidate data for HR analytics. Some of the most common methods include:
- Data standardization: This involves standardizing data formats, such as date and time formats, to ensure consistency across the data set. This also includes ensuring that data is entered in the same format, such as using a consistent format for phone numbers or email addresses.For example, when we examined the data specific to our customer, we found that employees of the contracted type were entered as both contracted and contractor by different units. To improve data quality, different entries are being standardized and included in the analysis.
- Data validation: This involves checking the data for errors, such as missing values, duplicate records, and outliers. By validating the data, organizations can identify and correct errors that could lead to inaccurate or unreliable insights. For example, It has been observed that in execute statements fed from multiple data sources or multiple company data, the training types are written in a repetitive but different syntax. When differences arising from writing, language, spacing, or usage are identified and subjected to transformation, the standardization of this data is achieved by singularizing and analyzing the actual same data. Here is another example, missing data often arises in data fed from multiple sources. For example, while employee type is one of the variables included in the analysis for one company, it was found that the entries for employee type were not made for the employees of another company fed to the same source. By starting from a different area where the missing employee data can be identified (such as the employee’s payment type), information about the missing contracted or standard employees can be obtained. For example, if an employee’s payment type is hourly, Peopleoma rule definition can ensure that this employee is recorded as a contracted employee.
- Data deduplication: Identifying and removing duplicate records from the data set. This is important because duplicate records can skew the results of the analysis and lead to inaccurate insights.
- Data normalization: Transforming the data into a consistent format that can be easily analyzed. This includes converting data from different units, such as converting salary data from hourly to annual or converting data from different measurement scales, such as converting data from categorical to numerical.
- Data consolidation: Combining data from multiple sources into a single, consistent data set. This is important because data from multiple sources can often be inconsistent or incomplete, which can lead to inaccurate or unreliable insights. To give an example, demographic and organizational information of employees comes from the central system for employees, performance results come from the performance system, and salary and benefits data come from the payroll system. Peopleoma data integration module collects data from different platforms into a single data pool and enables analysis on the same screen. In this step, we bring all the data together and analyze it with automation without the need for manual matching and merging. Another example, in one of the data sources, it was observed that the hierarchy data that is positioned at the top within the hierarchy 1-2-3 hierarchy is in hierarchy 1, while in another data source, it was determined that the hierarchy group positions at the top hierarchically is hierarchy 3. To perform the analysis correctly, the required hierarchical ordering was arranged and written to the correct location through Peopleoma transformation feature, and it was made possible to analyze it in a standardized way.
- Data governance: Creating and enforcing policies and procedures to ensure data quality and consistency. This includes creating data definitions, establishing data ownership, and creating data management plans. For example, it has been determined that exceptional employees have records in different companies simultaneously in inter-company assignments in multi-company structures, which causes duplication and corruption in the data. To prevent such situations, during the period in which the employee is assigned, they are considered exceptional in the previous job location and only counted in the active employee count of one of the two locations.
By using these methods of data cleaning and consolidation, organizations can ensure that the data used for analysis is accurate, consistent, and complete. This helps to improve the accuracy and usefulness of the insights generated from HR analytics efforts. Additionally, it helps to make the data more manageable and easier to work with.
It’s worth noting that, data cleaning and consolidation is an ongoing process that requires regular monitoring, maintenance and updates to ensure the data remains accurate and up-to-date. Organizations should establish a process for regular data cleaning, validation, and consolidation to ensure that the data used for analysis is always accurate and up-to-date. All these data standardization and validation steps are meaningful if they are sustainable. To ensure this sustainability, Peopleoma applies rules to the raw data in each update, standardizes it, and enables quick detection of entries that do not comply with the standard structure.
In conclusion, data cleaning and consolidation is an essential step in the process of HR analytics. By taking the time to thoroughly clean and consolidate data, organizations can ensure that the insights generated from their HR analytics efforts are as accurate and useful as possible. This will help organizations make data-driven decisions that can have a positive impact on their bottom line.